Good afternoon everyone and welcome to another View From the Top. It's my pleasure today to introduce our guest, Sam Altman, who is a co-founder and the CEO of OpenAI and one of the most prominent voices right now shaping how the world thinks about artificial intelligence.
As many of you know, Sam began his career as an entrepreneur, co-founding a company called Loopt before becoming an investor and an entrepreneur. He went on to serve as president of Y Combinator, where he worked closely with many early-stage founders and helped shape the next generation of startups. OpenAI has become one of the most closely watched companies in the world, and Sam's views on where this technology is headed continue to influence conversations across business, policy, and culture. Today, Sam is going to share the perspective he has built at the center of one of the most consequential technological shifts of our time. We could not be more happy to have him with us today. Please join me in welcoming Sam Altman.
Hi Sam.
We are all so excited to have you here. You know, one of the things I've been doing to get ready is I've been talking to an AI version of you, built by Codex, of course. And it's been very helpful. It answered most of my questions. But I have to say, I still don't think anything beats an in-person conversation. So, thank you.
Sam, many in this audience know you left Stanford to build your first startup, but fewer probably know that you almost did an internship in investment banking instead.
Could you take us back to that decision and your mindset at the time?
You know, I never wanted to be an investment banker, but it was what all the cool kids wanted to do, and I like fell victim to peer pressure. No, if any of you want to be investment bankers or are, like, more power to you. It was just not like my organic thing. I was like a nerd. But I got to Stanford and all of like the smartest kids were trying to like get to be investment bankers and that was the cool thing, and I was like, well, I want to be a smart kid. Maybe I should want to go into investment banking, too. So, for no reason other than peer pressure, literally, I like did the whole thing and got an internship and felt super cool. At the time, they would like fly you out to New York and there was this interview and then they have this like super nice dinner and it was like this very, like a 19 or 18, whatever I was, very impressive. I'd like never experienced anything like that and that made me even more think like this must be the right path for me.
And then I had this like increasing feeling of dread, and I just didn't think I was going to like it. Meanwhile, I had been like building this sort of, not even a startup at the time, but this like project with some friends. And then Y Combinator was like, "We will give you money for the summer to like live on and you can like try to do a startup." And I was like, "Okay, this is like a sign from the universe. I should try to go do that."
And you joined the very first batch of Y Combinator and eventually became president of YC. What did that experience teach you going from running a startup to running the world's most famous startup accelerator?
I mean, one of the best things for me about doing a startup... I was sad to drop out of school because I loved school and I was also worried I just was not going to keep learning stuff. And one of the coolest things for me about doing a startup is it was like, and still is, the highest like rate of learning environment I had ever been in. I think if you're like, this is maybe the wrong room to say this in, but a thing that someone said to me, I think it was maybe Paul Graham, maybe someone like that, said to me around then is, you will get an entire MBA in one month of doing a startup. And you know, I don't have an MBA, so I can't say whether or not that's true, but I did learn a lot very quickly about business and product and customers and sales and all of this other stuff that has been very important to the rest of my career that I think I just wouldn't have been able to learn in school. It was one of these things that like you can just learn by doing, and maybe there's no other good way to learn it. So the rate of learning for that first period of a startup was like unbelievable. I definitely have never learned more in any other period in my life.
Running YC was its own kind of learning. That's like almost like a government job. You don't have like the power over these companies, but you're trying to like build a healthy community and make sure everyone thrives. So that was another interesting and very different learning experience. And like everything that comes before, and you'll all find this out in your own lives, like you build on everything no matter how unrelated they seem. So like I learned a lot of things about running a startup that were helpful for YC, but it was a very different job.
How did you go about learning all of those skills that were so different than running a startup? Like, do you feel like you learned them on the job or you had mentors? How did you actually go about that?
I mean, definitely had great mentors, but there's no... I think if a job has been done many times before, you can maybe learn it in a classroom. Maybe you can learn a lot of skills that are helpful and a lot of sort of knowledge that's helpful.
But I think things like any specific startup or running Y Combinator or any other kind of like not-that-many-people-have-done-it jobs, you're just going to learn as you go. And I am a big believer that people spend too much time trying to prepare to do the thing than just doing the thing and learning as they go. So, like, you're going to make a lot of mistakes, you're going to learn very quickly. As long as you really approach it with like a spirit of, I think people call it deliberate practice, where you are running a lot of experiments and then actually reflecting on the learnings, positive or negative, and trying to update your model and not just sort of like random flailing. I think the rate of learning is super high and people almost always surprise themselves on if you just like jump in and do the thing how quickly you can get good at it.
YC gave you a front row seat to all kinds of ambitious ideas. Yet in 2015, when you co-founded OpenAI, most people told you artificial general intelligence was impossible, at least for the foreseeable future. What do you think you and your team saw then that other people were missing?
Deep learning was working. So I think it... I actually, when I was a student at Stanford, I worked in the AI lab and I like was planning to work on AI for my career. And I remember very memorably in 2005 a Stanford CS professor telling me that the one thing we knew for sure, there were a lot of things about AI, it was probably going to take another century at least, but the one thing we knew for sure is deep learning was not going to work. And you know, it's like, okay, I believe you. You're like, you know, impressive. And then in 2012, there was the famous AlexNet result. And then by 2014, we had seen that deep learning seemed to get better with scale in all these different ways. And the world didn't accept this at all. It's actually hard to overstate how much the world was just like, there is no way this works. You know, there are still a few like people online that you can find that will spend all their time talking about how deep learning is not going to work and it's a parlor trick and this and that.
At this point, that's sort of the radical view. At the time, the radical view was there is no way this is going to get to any serious level of intelligence. This is a ridiculous thing.
But I had kind of learned, and this has been, to the degree there has been a central sort of insight of my career, that there are surprising effects to scale and that when you see a beautifully scaling exponential of some sort, you are supposed to keep going. And something about human intuition does not do a good job with that. And in all of these different things, the scaling returns of individual companies, networks like Y Combinator, neural networks, lots of other examples I could give, but we'll skip in the interest of time. I have just always found this, and it is something that I have like developed a high personal conviction to continue to bet on. And so when we saw that this was working and we saw that it got better with scale, we didn't know at the time how predictably better it got with scale, that came a few years later. It seemed like we should keep pushing on it. And you know, maybe it would break at some point. We weren't sure, like there were various times where we almost lost conviction and thought we were hitting some sort of wall, but we just kept going. And it was so non-obvious to pretty much anyone that was not inside the walls of OpenAI at that time. Didn't seem to believe it. That's not literally true, there were obviously people who did, but it was like, it was a very unpopular opinion.
OpenAI starts as a nonprofit research lab with no intention of becoming a product company, and today over a billion people use ChatGPT every week. How has your own leadership had to evolve as OpenAI has gained tremendous influence?
Like many other tech people in the mid-2010s of Silicon Valley, I got enamored with the idea of research labs. This is a time when like everybody was talking about how Silicon Valley had, you know, lost the plot on research and we needed to get, you know, back to Xerox PARC or Bell Labs or whatever your favorite example was. And it was like kind of a cool thing to talk about and do. I always thought research was awesome. I always kind of wanted to run a research lab and I was always interested in AI. So this was kind of supposed to be my retirement project.
And for the first like 5 years, I mean, truly the coolest job I could imagine. It was like there was a small set of us. It was a research lab. We were right about many things but we were wrong about the way that we thought AI was going to go once developed. And so it truly hadn't occurred to us that the most likely path was that we were going to have to build a product company. We mostly thought that we were going to like develop this unbelievable super intelligence. And I realize this sounds ridiculous now, and that we wouldn't then have to do all of the hard work of figuring out how to get it usefully into people's hands and for society to benefit. It was like there was that meme from a long time ago of like, do the first thing, you know, do the second thing, question mark, question mark, question mark, profit, about how like startups were going to work. Ours was like, do research, build super intelligence, many question marks, people are benefit and happy.
And the technology went in a very different path than we originally thought it was going to. I think it's like a much better one, but it means we did have to productize this. And it also means, and this is one reason I think it's a good direction, is that this is much more like a normal economic force than we imagined at the time. This is like electricity or transistors or some other major scientific discovery that enables many other companies, technologies, scientific discoveries to happen. And we have to put infrastructure, which is building products like ChatGPT, it is also building like an amount of compute bigger than any project humanity has ever taken on in history, into people's hands.
So it became clear by, I would say, late 2020, early 2021, that we were going to have to build a big company. Didn't think it would happen so quickly. And then when we launched ChatGPT, which was like almost four years ago, then overnight everyone was like, okay, we're going to be a big company very fast.
A very big company you are. I'm curious to know what sorts of decisions are hardest for you to make today.
I think that would take the rest of our time. I'd be happy to talk about the whole, but the number one one that comes to mind is this, people call it different things: pausing, pacing, intelligence speed limits. But like fundamentally, if you believe, which I very much do, that people always should be in control of the future and we should not either intentionally hand over irreversibly decision-making power to AI or accidentally lose control. And also, you believe that power needs to be pretty distributed in the future. Both of which are saying a different version of like we want the kind of democratic pluralistic ideals that have gotten us this far to continue, and we don't want to see too much power concentration either in an AI with IQ 10 billion or in, you know, a small number of people that have AI and can do whatever they want, everybody else can't. If you believe both of those,
then I think you get to very difficult questions about how much smarter than the smartest people can we let an AI system get, how quickly, and, you know, how much do we need to enhance people's ability to be smarter or to understand these systems? And I think this is solvable. It just takes time.
And then if you believe that, there's like a limited number of ways that I think the world can come together and agree on how we're going to say what those limits are, and how we're going to handle them. And these are very tricky because you have extremely strong opinions on all sides. You have people saying like any regulation is bad, which I think is wrong. People saying that we have to like absolutely stop all progress right now, which I think is absolutely wrong. And like in many other of society's highest-stakes debates, trying to like be the pragmatic centrists in the middle of this is a very difficult place to be in.
How has your own thinking about being a pragmatic centrist and AI safety evolved over the past six months as you've seen capabilities accelerate?
You know, I still believe in a lot of our earliest tenets about how we think AI should get deployed in society. I still believe in iterative deployment. I still believe that society has got to contend with this technology as we develop it, even though it's, you know, difficult and an incredible rate of change. And I still believe that people are incredibly resilient and adaptable and honestly don't care that much about the work of machines. They're just so focused on people that the future is going to look, even if the technological landscape of the future looks very different, that the sort of societal shape of the future is going to be much more similar to today than people think.
All that said, watching the rate of progress the last six months has tested the strength of my convictions on some of those things. Like it is, you know, in some narrow senses, like certainly mathematical reasoning as far as I'm concerned, we have super intelligence now, and I didn't think it was going to happen this fast.
And if I play out another year or two of a similar rate of progress, which I think without any changes to the current landscape in the world and sort of the alignment at the level of companies and governments and societies we will have, then it seems to me like that's not going to work to make no changes to how we do things now. Like the systems are just getting so capable so quickly that if we want to hold on to those two things I mentioned a little bit ago, we're going to have to make some changes to how we think about pacing progress from here.
People have very strong opinions about this. I'm curious, like, how does that debate play out inside of OpenAI today?
I'd say the more people see and feel how good the models are getting, the less spread there is in the debate, and the more people are like, "Okay, we're going to have to think about like the strategy for the future and what it takes to make the safety guarantees we want to make somewhat differently than we did in the past."
I realized that a lot of the debate inside of OpenAI, or a lot of the difference in opinion inside of OpenAI, and I think it's also a good learning for the difference in opinion in society, is there was like a lot of debate about how good the models were going to get, how quickly. That got reframed as a debate about what the right strategy was. Once that debate is answered and it's like, okay, the models are about to get extraordinary, then there's less space on the what should we do about it questions. Still some.
You recently spoke at the UN about AI safety and you emphasized the importance of the government when it comes to regulating AI, but traditionally policy and technological change move at pretty different paces. So, what do you think are some ways that AI labs and the government can better work together?
I'm actually pretty happy with how it's trending. Not recently, I didn't think most people in most governments took it seriously enough. The last few months it's changed, and now like I think the conversation is being, we'll see if something actually happens, but I didn't think anything good was on a trajectory likely to happen, and now I think probably yes.
There's also this question about AI safety at an individual or organizational level. What can people or business leaders do today to better prepare themselves for AI's potential risks?
There's so many levels of that. I think the one, just so that I don't go through a laundry list of 30 things, the cybersecurity... I don't even know what a dramatic enough word to use, but the problem coming towards us, I think is still something that we have not, as organizational leaders or countries or individuals, taken seriously.
There was like a moment earlier this year where it was like the Mithros freak out or whatever people called it, and I think things did get somewhat better. Like people definitely patched some bugs, but it was another one of these cases where people didn't really intuit the exponential. So it's like, okay, there are these models, they can find a bunch of security vulnerabilities. Those are probably all of the security vulnerabilities that exist, or all the security vulnerabilities that models are ever going to find. And I don't think most people sort of said, "Okay, the models are going to get much smarter. They can probably find bugs like crazy bugs, you know, bugs that are outside of the normal threat model of what we think a hacker can do. You know, hardware-level insane stuff, whatever." And so people fix some bugs and, you know, security professionals still freaking out, CISOs still freaking out, but largely speaking I think most organizational leaders have somewhat moved on.
And the models have kept going. And I no longer think we're going to be able to just patch all the bugs. I think we're going to have to completely switch the defense model to sort of using the same technology for sort of a new kind of defense shield. And I think it's a huge safety and security issue. And I am somewhat astonished at how relatively little attention it's getting.
And to bring it back to the people in this room, you know, as business school students at Stanford, we're right by the people building the future of AI, but it's not always clear where we fit in. So, what advice would you give us on how we can shape what comes next?
My honest belief is that it's not going to be as different as quickly as people think. Like it is totally true that huge numbers of tasks that are done today by people will be done better, cheaper, faster, you know, completely differently by AI. And it's also true that wide swaths of current jobs will just, the way they're done today at least, will be entirely done by AI.
It's actually been interesting to watch over the last 10 days the reaction of the mathematical community. You know, the day zero reaction was like, this is fake. The day one reaction was like, we're cooked, and our field is over, and, you know, we're telling our grad students to switch majors. The day three reaction was maybe like, it's horrible at, I did this and no one should be allowed to, you know, if this is the price of understanding more mathematics we don't want it. But then by kind of maybe day five it shifted a lot, and I started hearing from mathematicians who were like, yes, there is one kind of math job that is over, but mostly what we did was not solve Millennium Prize problems, most of us never did that, most of what we did was figure out how to make sense of knowledge, how to communicate it, how to like present it in a human-intelligible way. And we've now seen a lot of people say, actually, I'm going to have a different but hopefully better kind of career as a mathematician where I'm going to, I get that AI is going to like drive mathematical discovery from here to a significant extent. But in terms of figuring out what to work on, what it means, how to present it, how to explain it, how to like write a great proof, how to do all these other things, you know, I think there's going to be a whole new set of stuff.
And that was the fastest that I think we've all gotten to watch a field go through this cycle, but versions of it happen more slowly in other fields. You know, I'm sure many of you watched what happened the last two years of computer programmers saying, "Our job is over," to actually like, looks like we're going to keep paying ever higher salaries to people that can get computers to do things. It's just the day-to-day of the job is going to be different. Consultants went through a period where they were like, "This is totally over." And then they found out that actually like no matter how good the AI models are at making like nice-looking PowerPoint slides, which they are good at, people like really want to interact with another person, and actually almost no one wants the AI consultant to show up and tell them what to do.
You get the point. This is going to keep happening. So I don't think there's going to be like a job apocalypse in the way that many people talk about. There will be huge amounts of short-term change in terms of the tasks people do and some kinds of jobs. But if you are like heading into the world right now to go work on something, I think you want to be starting your career at a time of like the highest degree of change and you want like the most open playing field. I think people with like potential and drive and ideas and sort of a new way of looking at things and new skills do relatively the best when the ground is shaking the most. My like chronic disappointment in MBA students is that they always want to just like go do the thing that was cool like five years ago and not do the new thing. And I think this is a great time to go do the new thing.
So go do the new thing, even if AI is the cool thing right now.
